Does volume matter - a retrospective study (Read 355 times)

Purpose: To determine if the volume of miles run in a year has a significant impact on distance running performance

Methods: Subject ran a ton over an 11-year period and logged each run. Cumulative miles for each calendar year were totaled. Since a single race can be affected by many variables, each race run was converted to its 10K equivalent (in seconds) to make comparison possible, and the best race for each year identified. This allowed best race performance to be plotted as a function of miles run that year.

Results: See figure. At least for this subject, race performance is highly correlated with volume run. While the year of each data point is not shown, they are not in chronological order. When a high mileage year was followed by a low mileage year, race performance typically reflected the lowering in mileage.

Correlation does not equal causation, but the strength of the trend is certainly noteworthy.

"If you want to be a bad a$s, then do what a bad a$s does. There's your pep talk for today. Go Run." -- Slo_Hand

- For each of those races, make the corresponding mileage data point equal to the 3-month rolling average of the miles you'd been running.

2) Control for intensity: This is, of course, the meat of the "debate" over volume. For each race, in addition to getting your 3-month rolling average of miles, get your average number of workouts per week during those months. This would actually be really valuable, and I've never seen anyone do this (I'm sure it's been done, but I'd love to see it).

I have made similar observations about my "subject's" marathon times and the mileage in the 16 weeks preceding, and the correlation is quite strong. The trouble with the marathon, though, is it also produces outliers due to uncontrolled variables such as, oh, I don't know, strep infection a few days before the race, 85 degree days, and what not.

I am not sure what conclusion to draw from this: it could be argued that I would have run even faster in 2012 if I had sustained 3500ish level mileage. I don't know about that; my body didn't seem to agree, but maybe it would have adapted.

- For each of those races, make the corresponding mileage data point equal to the 3-month rolling average of the miles you'd been running.

2) Control for intensity: This is, of course, the meat of the "debate" over volume. For each race, in addition to getting your 3-month rolling average of miles, get your average number of workouts per week during those months. This would actually be really valuable, and I've never seen anyone do this (I'm sure it's been done, but I'd love to see it).

1) More data, and more granular data, is not always good. Bring in course variations, weather, etc, and the data points will be all over the place. Mileage has slow and long-term effects on fitness, so it makes more sense to assess its effects via a long-term output (best race in a year) than adding in all that variability which is more likely due to other factors. Say I ran a 34:10 one week and a 33:09 the next (done that), it really isn't rational to look at those two data points and think mileage has anything to do with it over 1 week separation. One could average the best 3 races or something like that to smoothen out outliers.

(MTA: I'm not sure this is clear, but say I run 5 races in a month with a minute spread on the times. There likely won't be any meaningful difference in the 6-month rolling average for each race, certainly not to explain that big of a difference. All we would be doing is pulling in the effects of other variables, which is exactly what I was trying NOT to do. I believe taking your best race of a given cycle is most representative of the fitness of that cycle....it's easy to under-perform but you can't over-perform your fitness. You may approach it a bit more closely one cycle vs another, but it's a good metric to try and isolate the effect of mileage from everything else)

I do totally agree with the rolling average, however. Given that all 11 years of these data were on paper logs, however, that's not as easy to do. I had already added up the yearly total each year and written it down. But I'd pull the prior 6 months at least, prior 3 months is not going to be very meaningful (did I say mileage is slow to create all its benefits already?).

2) Workouts per week would not control for intensity well. It was always 2 or 3, it's not the number but the content. And that content is much harder to quantify in an analyzable way -- can't really turn it into a number. One could apply average pace (I do have that data), but again that is not a very meaningful number at all. Coasting along at 6:30 pace, versus averaging that by doing easy runs at 7:00 and doing tempos and intervals, are two very different things but would not differentiated by average pace.

I now have more years of data I could use (IIRC this covered 1997-2007), but I did not because I've since added an additional variable that would confound the results....not racing regularly and hard. During that 11 year period I raced frequently and was always working to do the best I could.

"If you want to be a bad a$s, then do what a bad a$s does. There's your pep talk for today. Go Run." -- Slo_Hand

I am spaniel - Crusher of Treadmills

NHLA

posted: 12/20/2013 at 6:42 PM

Sorry I don't understand your graft. What does up & down and sideways mean?

I know I don't run well unless I run 50 mpw if I run 80 mpw I get hurt.

Sorry I don't understand your graft. What does up & down and sideways mean?

I know I don't run well unless I run 50 mpw if I run 80 mpw I get hurt.

The X-axis is 10K equivalent, in seconds. In other words, if the best race of the year was a half marathon I put that into the McMillan calculator and took the 10K equivalent. It it was a 10K, no conversion was required. The Y-axis is miles run in the calendar year. So the trend you see (sideways?) is that there is a very strong correlation between the volume of miles run and faster race times.

"If you want to be a bad a$s, then do what a bad a$s does. There's your pep talk for today. Go Run." -- Slo_Hand

The X-axis is 10K equivalent, in seconds. In other words, if the best race of the year was a half marathon I put that into the McMillan calculator and took the 10K equivalent. It it was a 10K, no conversion was required. The Y-axis is miles run in the calendar year. So the trend you see (sideways?) is that there is a very strong correlation between the volume of miles run and faster race times.

Heh. Yes, I suppose I should lose a degree just for mixing that up. Yes, X-axis is miles per year and Y-axis is 10K equivalent in seconds. Sorry, hard to post with a 2-yr-old trying to post at the same time.

"If you want to be a bad a$s, then do what a bad a$s does. There's your pep talk for today. Go Run." -- Slo_Hand

... I believe taking your best race of a given cycle is most representative of the fitness of that cycle....it's easy to under-perform but you can't over-perform your fitness.

....

I was also going to say that I might average all of my relatively flat road races that I thought were fast, instead of just picking the top. But your point above seems plausible - one is probably pretty unlikely to vary much above one's actual physical ability. -- except maybe due to course being short -- but presumably one should either adjust for that or exclude that as a candidate data point.

But I'm not sure I have the energy to plot this for myself, and also my own history lacks sufficiently useful history and variation in mileage (I realize as I consider it).

wouldn't this make more sense if the mileage totals were for the 365 days (or whatever period) prior to the race, instead of the calendar year?

also, what about the cumulative/carry-over effects? if someone ran 3000 miles a year, and took it down a notch and ran a consistent 1500 miles the next, he'd still be in better shape than if he had ran 2000 that year with a 1500 year before?